Method and heating apparatus for estimating status of heated object

ABSTRACT

The present disclosure relates to an apparatus and a method for estimating a state of an object to be heated based on sound which is generated when the object to be heated is heated, and providing the estimated information to other devices in an Internet of Things (IoT) environment through a 5G communication network. The heating apparatus may include a housing having a receiving space therein, a heating member disposed within the housing, a power supplier for supplying power to the heating member, a top plate disposed on the top of the housing to support the object to be heated, a sound sensor disposed on the bottom of the top plate, and a controller for predicting the state of the object to be heated by using a deep neural network model that has been trained through machine learning based on a sound signal received from the sound sensor.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Divisional of U.S. application Ser. No.16/674,707, filed on Nov. 5, 2019, which claims priority under 35 U.S.C.§ 119(a) to Korean Application No. 10-2019-0087796 filed in the Republicof Korea on Jul. 19, 2019, the contents of which are incorporated byreference herein in their entirety.

BACKGROUND OF THE INVENTION Field of the Invention

The present disclosure relates to a heating apparatus and method forestimating a state of an object to be heated. More particularly, thepresent disclosure relates to an apparatus and a method for estimating astate of an object to be heated based on sound which is generated whenthe object to be heated is heated.

Discussion of the Related Art

When food is heated for purposes such as cooking, if heating continueswithout continuous monitoring, the food may be overheated and thusevaporate, and in a serious case, a fire may be caused. Accordingly,technical attempts have been made to sense when the temperature risesexcessively, and to automatically control or stop operation of a heaterin such instance.

In Korean Patent Registration No. 1390397, entitled “Apparatus andmethod for controlling safety cooking appliance,” a method for reducingthe amount of heating when food boils over by installing a CCD camera ona main body of the heater to photograph an image of the food to becooked, and analyzing the photographed image, is described. In order toimplement the technology disclosed in the above-described document,installation of a separate CCD camera so that the heater may be viewedfrom the top is necessary, and there is a limitation in thatconsiderable processing resources are required to analyze thephotographed image.

In Korean Patent Registration No. 1723601, entitled “An apparatus andmethod to predict and detect a fire on cooking ranges” described is amethod for warning a user when a warning time is reached by calculatinga warning time based on a temperature variation pattern according to atemperature sensing value received through a remote temperature sensorfor sensing the temperature of a cooking container. In order toimplement the technology disclosed in the above-described document, theremote temperature sensor for sensing the temperature of the cookingcontainer should be newly added, separately from the heater, and thereis a limitation in that estimation may be inaccurate since the warningtime is calculated based on the temperature variation pattern based onthe temperature of the cooking container rather than the contents in thecooking container.

In Korean Patent Registration No. 1849099, entitled “Boil and boil drydetection apparatus,” described is a method for determining that wateris boiling by determining vibration of a cooking container using anultrasonic wave transmitting apparatus for transmitting a transmissionultrasonic wave signal toward the cooking container placed on a cookingdevice, and an ultrasonic wave receiving apparatus for receiving areflection ultrasonic wave signal reflected and returned from thecooking container. In order to implement the technology disclosed in theabove-described document, an ultrasonic wave transmitting apparatus andan ultrasonic wave receiving apparatus should be additionally provided,separately from the heater, and there is a limitation in that theaccuracy of estimation may be lowered since the frequency of vibrationis different for every different container.

In order to overcome the above-described limitations, there is a need toprovide a more advanced solution regarding a method for sensing theheating situation in the process of heating an object, such as whencooking, and automatically controlling the heating operation.

SUMMARY OF THE INVENTION

Accordingly, one aspect of the present invention is to solve theabove-noted and other problems in which, in addition to a heater,components such as a camera, an ultrasonic apparatus, and a remotetemperature sensor apparatus should be additionally installed in orderto monitor the process of heating an object so as to prevent excessiveheating.

Another aspect is to address a shortcoming in which the accuracy ofdetermination is lowered unless an additional apparatus is used inaddition to a heater in determining whether the contents of a containerheated by the heater are boiling.

Still another aspect is to address a shortcoming in which an additionalapparatus for directly measuring the temperature of an object to beheated should be used in addition to the heating apparatus so that thecooking of the object to be heated is performed for a specific timewithin a certain temperature range.

Accordingly, one object of the present invention is to provide a heatingapparatus for estimating a state of an object to be heated by sensing asound which is generated when the object to be heated boils.

Embodiments of the present disclosure provide a heating apparatus and amethod capable of estimating the state of an object to be heated evenwithout separately adding, in addition to the heater, components such asa camera, an ultrasonic apparatus, and a remote temperature sensorapparatus, unlike the related art. Further, the embodiments of thepresent disclosure provide a heating apparatus and a method capable ofaccurately determining whether the object to be heated is boiling evenwithout using an additional apparatus in addition to the heatingapparatus.

In addition, the embodiments of the present disclosure provide a methodfor disposing a sound sensor by which sound which is generated duringheating of the object to be heated can be reliably collected. Further,the embodiments of the present disclosure provide a method formaintaining a boiling state only for the time desired by the user afterthe object to be heated has reached the boiling state, therebyimplementing safe and effective cooking.

Also, the embodiments of the present disclosure provide a heatingapparatus and method capable of preventing the object to be heated fromboiling over by adjusting the magnitude of heat energy provided throughthe heating apparatus after the object to be heated has reached theboiling state, thereby providing convenience and stability to the user.

The effects of the present disclosure are not limited to theabove-described effects, and other effects not described may be clearlyunderstood by those skilled in the art from the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of the presentdisclosure will become apparent from the detailed description of thefollowing aspects in conjunction with the accompanying drawings, inwhich:

FIG. 1 is a diagram illustrating the environment in which a heatingapparatus for estimating a state of an object to be heated according toan embodiment of the present disclosure operates;

FIG. 2 is a diagram illustrating sound generated while the object to beheated is heated by the heating apparatus according to an embodiment ofthe present disclosure;

FIG. 3 is an exploded view of the heating apparatus according to anembodiment of the present disclosure;

FIG. 4 is a block diagram of the heating apparatus according to anembodiment of the present disclosure;

FIG. 5 is a diagram illustrating a position where a sound sensor isdisposed in the heating apparatus according to an embodiment of thepresent disclosure;

FIG. 6 is a flowchart illustrating an operation of a heating apparatusaccording to another embodiment of the present disclosure;

FIG. 7 is a flowchart illustrating an operation of a heating apparatusaccording to still another embodiment of the present disclosure;

FIG. 8 is a diagram illustrating an operation of automaticallycontrolling power after sensing the boiling state of the object to beheated in the heating apparatus according to an embodiment of thepresent disclosure; and

FIG. 9 is a diagram illustrating a deep neural network model forpredicting the state of the object to be heated used in the heatingapparatus according to an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Advantages and features of the present disclosure and methods ofachieving the advantages and features will be more apparent withreference to the following detailed description of example embodimentsin connection with the accompanying drawings. However, the descriptionof particular example embodiments is not intended to limit the presentdisclosure to the particular example embodiments disclosed herein, buton the contrary, it should be understood that the present disclosure isto cover all modifications, equivalents and alternatives falling withinthe spirit and scope of the present disclosure. The example embodimentsdisclosed below are provided so that the present disclosure will bethorough and complete, and also to provide a more complete understandingof the scope of the present disclosure to those of ordinary skill in theart.

The terminology used herein is used for the purpose of describingparticular example embodiments only and is not intended to be limiting.As used herein, the singular forms “a,” “an,” and “the” may be intendedto include the plural forms as well, unless the context clearlyindicates otherwise. The terms “comprises,” “comprising,” “includes,”“including,” “containing,” “has,” “having” or other variations thereofare inclusive and therefore specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof. Furthermore, the terms such as “first,” “second,” and othernumerical terms may be used herein only to describe various elements,but these elements should not be limited by these terms. Furthermore,these terms such as “first,” “second,” and other numerical terms, areused only to distinguish one element from another element.

Hereinafter, embodiments of the present disclosure will be described indetail with reference to the accompanying drawings. Like referencenumerals designate like elements throughout the specification, andoverlapping descriptions of the elements will not be provided.

FIG. 1 is a diagram illustrating the environment in which a heatingapparatus for estimating a state of an object to be heated according toan embodiment of the present disclosure operates. Although a heatingapparatus of the present disclosure includes various devices havingheating mechanisms, the heating apparatus will be described below as anelectric range as an example, for convenience of explanation.

As shown, an electric range 1000 can operate in an Internet of Things(IoT) environment constructed by using a 5G communication network. Theelectric range 1000 can communicate with an artificial intelligencespeaker 3000, a user terminal 5000, and an external server 7000.Further, the user terminal 5000 can receive a certain command from theuser and transfer the command to the electric range 1000, and receiveoperation information of the electric range 1000 and transfer theoperation information to the user.

The user terminal may include a communication terminal capable ofperforming the function of a computing apparatus, and may be a desktopcomputer, a smart phone, a notebook, a tablet PC, a smart TV, a portablephone, a personal digital assistant (PDA), a laptop, a media player, amicro server, a global positioning system (GPS) apparatus, an electronicbook terminal, a digital broadcast terminal, a navigation, a kiosk, aMP3 player, a digital camera, a consumer electronic, and other mobile ornon-mobile computing apparatuses, which are operated by the user, but isnot limited thereto. Further, the user terminal may be a wearableterminal such as a watch, eyeglasses, a hair band, and a ring, having acommunication function and a data processing function. Such a userterminal is not limited to the above terminals, and the terminalscapable of voice recognition may be borrowed without limitation.

The artificial intelligence speaker 3000 can also receive a certaincommand from the user through voice and transfer the command to theelectric range 1000, and also receive operation information of theelectric range 1000 and transfer the operation information to the userby voice. The external server 7000 can also receive and store theoperation information of the electric range 1000, and also provide areference for the electric range 1000 to perform determination throughan accumulated database.

For example, the external server 7000 may have an object-to-be-heatedsound database in which, for each type of object to be heated,information about the sound generated while the object is heated isstored in association with the temperature of the object. The electricrange 1000 can collect the sound which is generated while heating theobject, and estimate the state of the object by referring to theobject-to-be-heated sound database through communication with theexternal server 7000.

As another example, the external server 7000 may have anobject-to-be-heated sound deep neural network model that has beentrained in advance to estimate the state of the object to be heatedbased on the sound signal which is generated as the object is heated. Inthis instance, the electric range 1000 can collect the sound which isgenerated while heating the object, and also estimate the state of theobject to be heated by using the object-to-be-heated sound deep neuralnetwork model through communication with the external server 7000.

As still another example, the external server 7000 can update theobject-to-be-heated sound database and the object-to-be-heated sounddeep neural network model by communicating with various electric rangesand collecting information. The external server 7000 can also transmitthe updated database and deep neural network model to the electric range1000 so that the electric range 1000 itself estimates the state of theobject to be heated based on the sound signal which is generated as theobject is heated.

In addition, the electric range 1000 can be connected to theabove-described devices through a network, and the network can serve toconnect the electric range 1000 with the user terminal 5000, theartificial intelligence speaker 3000, and the external server 7000. Sucha network may be a wired network such as a local area network (LAN), awide area network (WANs), a metropolitan area network (MAN), and anintegrated service digital networks (ISDN), or a wireless network suchas a wireless LAN, CDMA, Bluetooth, and satellite communication, but thescope of the present disclosure is not limited thereto.

Further, the network can transmit and receive information by using shortdistance communication and/or long distance communication. Here, theshort distance communication may include Bluetooth®, radio frequencyidentification (RFID), Infrared Data Association (IrDA), ultra-wideband(UWB), ZigBee, and Wi-Fi (wireless fidelity) technologies, and the longdistance communication may include code division multiple access (CDMA),frequency division multiple access (FDMA), time division multiple access(TDMA), orthogonal frequency division multiple access (OFDMA), andsingle carrier frequency division multiple access (SC-FDMA).

The network may include connections of network elements such as hubs,bridges, routers, switches, and gateways. The network may also includeone or more connected networks, including a public network such as theInternet and a private network such as a secure corporate privatenetwork. For example, the network may include a multi-networkenvironment. Access to the network may be provided through one or morewired or wireless access networks.

Next, FIG. 2 is a diagram illustrating sound generated while the objectis heated by a heating apparatus according to an embodiment of thepresent disclosure. Although the object to be heated may include varioustypes of contents contained in the container, the object to be heatedwill be described below as a liquid contained in a pot as an example.

As shown in FIG. 2 , as the liquid in the pot is heated, bubbles aregenerated in the liquid and the bubbles rise to the surface of theliquid, such that sound is generated from the pot or between the pot andthe top plate of the electric range 1000. In the graph of FIG. 2 , thex-axis represents time and the y-axis represents the magnitude of thesound. At the initial stage of heating, only the temperature of theliquid in the pot rises, and no bubbles are generated. Accordingly, nosound is sensed.

However, as the heating time passes, the number of bubbles generated inthe liquid contained in the pot increases, and the magnitude of thesound generated thereby increases. Accordingly, based on the magnitudeof the sound, it is possible to determine whether the object to beheated is boiling, and also to estimate the temperature and degree ofboiling of the object to be heated.

Based on this phenomenon, an apparatus capable of directly measuring thetemperature of the contents of the pot and a microphone capable ofcollecting sound generated from the pot on the top plate of the electricrange can be installed, and then the temperature of the contents and thesound generated at the corresponding temperature can be recorded whilechanging, for example, the type, weight, and size of the pot, and thetype, weight, and size of the contents contained in the pot.

The recorded data is sound data for which a corresponding temperature islabeled. A matching table capable of estimating the temperature of thecontents according to the sound generated from the pot on the top plateof the electric range can be made by using the labeled data. Inaddition, the deep neural network model capable of estimating thetemperature of the contents according to the sound generated from thepot on the top plate of the electric range can learn and be trained withthis labeled data.

Further, the database or the deep neural network model generated throughthis preliminary work can be embedded in a memory of the electric range1000, or stored in the external server 7000 with which the electricrange 1000 communicates, and be used to estimate the state of the objectto be heated according to a sound signal received from a sound sensor(e.g., vibration sensor) of the electric range 1000 during actual use.

Next, FIG. 3 shows an exploded view of the heating apparatus accordingto an embodiment of the present disclosure. The electric range 1000 forestimating the state of the object to be heated according to anembodiment of the present disclosure may include a housing 110 having areceiving space therein, a heating member 130 disposed in the housing110, a power supplier 140 for supplying power to the heating member, apower management part 150 for managing the power supplier 140, a topplate 120 disposed on the top of the housing 110 to support the objectto be heated, a sound sensor 170 (e.g., vibration sensor) disposed underthe top plate 120, and an interface 180 for receiving an instructionfrom the user. The sound sensor 170 may or may not contact the top plate120.

Further, the electric range 1000 may include additional sensors capableof sensing the operation situation of the electric range 1000 such as atemperature sensor 190 disposed on the bottom of the top plate 120 tosense a temperature, and a weight sensor 160 disposed within the housingto measure the weight of the object to be heated disposed on the topplate 120.

In addition, the electric range 1000 can include a controller forcontrolling power supply of the power supplier 140 to the heating member130 by controlling the power management part 150 based on the soundsignal received from the sound sensor 170. The controller can also stopthe power supply from the power supplier 140 to the heating member 130when it is determined, based on the sound signal received from the soundsensor 170, that the object to be heated is boiling and may boil overdue to an excessive degree of boiling.

Alternatively, upon receiving a signal according to which the object tobe heated is to be continuously boiled for a certain duration, from theinterface 180, the artificial intelligence speaker 3000, or the userterminal 5000, the controller can also increase the power supply of thepower supplier 140 when the sound signal is reduced to a threshold orless.

As shown in FIG. 3 , the sound sensor 170 can also be disposed justoutside a coil that is the heating member 130, and if a hollow is formedin the coil, the sound sensor 170 can be disposed to contact the bottomsurface of the top plate 120 at the position where the hollow is formed.

The sound sensor 170 can also be disposed to closely contact the bottomsurface of the top plate 120 in order to closely sense the sound fromthe object to be heated that is supported by the top plate 120, but mayalso be disposed to have a slight distance from the bottom surface ofthe top plate 120 according to the sensitivity of the sound sensor 170.Further, the sound sensor 170 may be an Electrets Condenser Microphone(ECM) capable of converting the sound to be collected into an electricsignal, and further, various types of microphones may be used.

Next, FIG. 4 is a block diagram of the heating apparatus according to anembodiment of the present disclosure. The heating apparatus according toan embodiment of the present disclosure may be represented by the blockdiagram as in FIG. 4 , and as shown in FIG. 4 , a controller 200 cancontrol the operation of various components in the electric range 1000.

When the sound sensor 170 collects the sound generated between the potand the top plate while the pot vibrates as the object to be heated isheated, the controller 200 of the object to be heated can control theamount of power supplied by the power supplier 140 according to thecollected sound signal.

When the sound signal indicates the object is boiling strongly and islikely to boil over, the controller 200 can cause the power supplier 140to temporarily stop the power supply. In another example, upon receivinga signal according to which the object to be heated is to becontinuously boiled during a certain duration, from the interface 180,the artificial intelligence speaker 3000, or the user terminal 5000, thecontroller 200 can also increase the power supply of the power supplier140 when the sound signal is reduced to a threshold or less.

In addition, the electric range 1000 may include the weight sensor 160in addition to the sound sensor 170. In particular, the weight sensor160 can acquire weight information of the object to be heated that issupported by the top plate 120 of the electric range 1000.

According to the weight of the object to be heated, a different sound orvibration may be generated even at the same degree of boiling of theobject to be heated. Accordingly, the controller can also consider theweight signal received from the weight sensor 160 in addition to thesound signal received from the sound sensor 170 in determining the stateof the object to be heated.

Further, the weight sensor 160 can be used to determine additionalcharacteristics of the object to be heated. For example, the controllercan use the sensed weight of ingredients added to the object to heated(e.g., water, a sauce, a pre-mixture, etc.) and control the poweraccordingly.

In one embodiment, if no additional weight change is sensed after apredetermined time period, the controller can reduce power supplied tothe heating member. That is, no additional weight change after apredetermined time period can be determined as the user is finishedadding ingredients, and the boiling time period has been completed.Thus, the controller can reduce the power and save wasted cookingresources.

In another embodiment, if an increased weight change is sensed duringthe predetermined time period, the controller can maintain the powersupplied to the heating member. That is, an increased weight during thepredetermined time period can be determined as the user is still addingingredients (and thus not finished adding ingredients). Thus, thecontroller can efficiently maintain the power. In still anotherembodiment, if an increased weight change is sensed during thepredetermined time period, the controller can increase the powersupplied to the heating member. That is, the increased weight indicatesadditional ingredients have been added, which is likely to reduce theboiling of the ingredients for a short period of time until full boilingis resumed. Thus, in this example, the controller can increase the powersupplied to the heating member, so quickly achieve the previous boilingstate.

In another embodiment, if a reduced weight change is sensed after thepredetermined time period, the controller can reduce the power suppliedto the heater member including turning off the power and also stopdetecting the vibration signal. That is, a reduction in weight after thepredetermined time period can be determined as the user has removedingredients (e.g., removing noodles from boiling water), and thusadvantageously reduce/turn off the power because the user has completedcooking the ingredients. This advantageously save wasted cookingresources.

In yet another embodiment, if a reduced weight between a first weightand a second weight is sensed, the controller can reduce the powersupplied to the heating member by a first amount, and if a reducedweight between the second weight and a third weight is then sensed, thecontroller can reduce the power by a second amount. That is, thereduction of weight from a first weight to a second weight can bedetermined as removing a first ingredient, and the reduction of weightfrom the second weight to a third weight can be determined as removing asecond ingredient. Thus, the controller can advantageously reduce thepower supplied to the heating member by a first amount when the firstingredient is removed, and by a second amount when the second ingredientis removed. This advantageously saves wasted cooking resources.

In still another embodiment, if an increased weight is sensed between afirst weight and a second weight, the controller can increase the powerto the heating member to maintain the boiling state. That is, this canbe determined as adding an ingredient which reduces the boiling state,so the controller can advantageously increase the power to accommodatethe addition ingredient.

Further, using the first and second weights, the controller canadvantageously increase the power by predetermined increments. Forexample, for a small weight change between the first and second weights,the controller can increase the power by a small value corresponding thesmall weight change. For a large weight change between the first andsecond weights, the controller can increase the power by a large valuecorresponding the large weight change. A table can be stored in thememory indicating power increase values corresponding one-to-one toincremental weight changes. A similar approach can be used whendecreasing the power to correspond with small or large reductions inweight.

Further, the electric range 1000 may include the temperature sensor 190in addition to the sound sensor 170. In particular, the temperaturesensor 190 can be disposed to contact the bottom surface of the topplate 120 to sense the temperature of the top plate 120.

The temperature sensor 190 can be used to determine the authenticity ofthe sound signal received from the sound sensor 170, and whether thereis an error in the sound signal. For example, when a sound signal, whichis generated when the object to be heated boils, is received from thesound sensor 170 but the temperature of the top plate 120 sensed fromthe temperature sensor 190 is lower than a certain temperature, thesource of the signal sensed by the sound sensor 170 may be another soundrather than the sound generated by the boiling of the object to beheated. Accordingly, the controller 200 can be configured to ignore thesound signal received from the sound sensor 170 when the temperature ofthe top plate 120 sensed from the temperature sensor 190 is lower than acertain temperature (for example, 70° C.).

Further, the electric range 1000 may include a memory 210. Inparticular, the memory 210 can store the database or the deep neuralnetwork model generated through the above-described preliminary work,and the controller 200 can estimate the state of the object to be heatedaccording to the sound signal received from the sound sensor 170 duringthe use of the electric range 1000 by using the database and the deepneural network model.

Further, the electric range 1000 may include a transceiver 220. Thus,the electric range 1000 can communicate with the user terminal 5000 orthe external server 7000 through the transceiver 220.

The external server 7000 can also receive and store the operationinformation of the electric range 1000, and provide a reference for theelectric range 1000 to perform the determination through the accumulateddatabase. For example, the external server 7000 can have theobject-to-be-heated sound database in which, for each type of object tobe heated, information about the sound generated while the object to beheated is heated is stored in association with the temperature of theobject to be heated, and the electric range 1000 can collect sound whichis generated while heating the object to be heated, and estimate thestate of the object to be heated by referring to the object-to-be-heatedsound database through communication with the external server 7000.

As another example, the external server 7000 can have theobject-to-be-heated sound deep neural network model that has beentrained in advance to estimate the state of the object to be heatedbased on the sound signal which is generated as the object to be heatedis heated. In this instance, the electric range 1000 can collect thesound which is generated while heating the object to be heated, and alsoestimate the state of the object to be heated by using theobject-to-be-heated sound deep neural network model throughcommunication with the external server 7000.

As still another example, the external server 7000 can update theobject-to-be-heated sound database and the object-to-be-heated sounddeep neural network model by communicating with and collectinginformation from various electric ranges. The external server 7000 canalso transmit the updated database and deep neural network model to theelectric range 1000 so that the electric range 1000 itself estimates thestate of the object to be heated based on the sound signal which isgenerated as the object to be heated is heated.

In order to estimate the state of the object to be heated so that theobject is not excessively heated, the electric range 1000 can firstperform an operation of heating the object to be heated disposed on thetop plate 120 of the electric range 1000, sense the sound generated bythe object to be heated through the sound sensor 170, and adjust theamount of power supplied to the heating member 130 of the electric range1000 based on the sound signal received from the sound sensor 170.

Further, the controller 200 of the electric range 1000 can determine thestate of the object to be heated based on the sound signal received fromthe sound sensor 170. Upon this determination, the controller 200 of theelectric range 1000 can estimate the state of the object to be heatedaccording to the sound signal received from the sound sensor 170 byusing the deep neural network model that has been trained in advance toestimate the state of the object to be heated based on the sound signalwhich is generated as the object to be heated is heated.

Further, since a different sound signal may be generated at the samedegree of boiling according to the weight of the object to be heated,the controller 200 of the electric range 1000 can determine the state ofthe object to be heated based on the weight signal received from theweight sensor 160 for sensing the weight of the object to be heatedsupported by the top plate 120 and the sound signal received from thesound sensor 170, when adjusting the amount of power supplied to theheating member 130.

Next, FIG. 5 is a diagram illustrating the position where the soundsensor is disposed in the heating apparatus according to an embodimentof the present disclosure. As shown in FIG. 5 , the bottom surface ofthe top plate 120 of the electric range 1000 can be processed to have anembossed shape 123. The embossed shape 123 is intended to disperse thepressure transferred downwards when the top plate 120 is pressed whilesupporting a heavy object to be heated.

However, in order for the sound sensor 170 to sensitively sense thesound transferred through the top plate 120, it is preferable for thesound sensor 170 to contact the top plate 120 with as large an area aspossible. Accordingly, a portion of the bottom surface of the top plate120 that contacts the sound sensor 170 can be formed to have a flatshape 125 through a grinding process, for example.

Accordingly, the sound sensor 170 can sensitively sense the soundgenerated by the object to be heated transferred through the top plate120, and accordingly, the controller 200 can more accurately confirm thestate of the object to be heated. In addition, the sound sensor 170 mayinclude a portion for sensing sound and a connector 173 for transferringthe sensed sound as an electrical signal.

In addition, the connector of the sound sensor 170 can be connected to aPCB or the like disposed inside the housing 110, and accordingly, asound signal can be transferred to the controller 200. Further, thecontroller 200 can send an alarm signal to the user terminal 5000 or theartificial intelligence speaker 3000 through the transceiver 220, whenit is determined, based on the sound signal received from the soundsensor 170, that the object to be heated is boiling.

Since the user who receives the alarm signal through the user terminal5000 or the artificial intelligence speaker 3000 recognizes that thefood that he/she is cooking is boiling, it is possible to prevent adangerous situation, which is caused by neglect, from occurring. Inaddition, the user can be informed of the situation occurring in theelectric range 1000 through the user terminal 5000 even while at aremote location, and accordingly may remotely confirm and control theoperation of the electric range 1000.

Next, FIG. 6 is a flowchart illustrating an operation of a heatingapparatus according to another embodiment of the present disclosure. Inparticular, FIG. 6 illustrates when the user tries to boil ramen byusing the electric range 1000. The user can first make a voice commandsuch as “Connect the electric range” to the user terminal 5000, so thatthe electric range 1000 is connected to the user terminal 5000.According to this voice command, the user terminal 5000 sends aconnection command signal to the electric range 1000 (S110).

The electric range 1000, having received the connection command signal,performs communication initialization and connection with the userterminal 5000 (S120). The user terminal 5000, having received aconnection confirmation signal, can perform a voice report of “Theelectric range has been connected” to the user. The user who hasconfirmed that the user terminal 5000 and the electric range 1000 havebeen connected can say “Power level 8, ramen,” corresponding to thecooking that he or she is planning, thereby expressing the intention toboil water for cooking ramen at a power level 8.

Accordingly, the user terminal 5000 can send, to the electric range1000, a signal that sets the power level to 8 and sets a timer for theramen (a timer set to boil water for a further 4 minutes after the waterinitially boils, or a timer set to boil the water for a further 4minutes after the ramen has been added by, after the water initiallyboils, sensing when the ramen is added via the weight sensor) (S130).While sending the signal, the user terminal 5000 can notify the user ofthe details of the timer to be set by outputting, by voice, “When thewater boils, a 4-minute timer will be automatically set.”

When receiving the signal, the electric range 1000 can start anoperation at power level 8 after confirming, through the weight sensor,whether the container containing the object to be heated has been placedon the top plate 120 (S140). While heating water by supplying power tothe heating member 130, the electric range 1000 can sense that the wateris boiling by receiving the sound signal in the above-described manner,and inform the user terminal 5000 that the water is boiling (S150).

When the user terminal 5000 receives a signal indicating that the wateris boiling (a boiling signal), the user terminal 5000 can inform theuser that “The water is boiling. Please put in the ramen.” In responsethereto, the user can inform the user terminal 5000 that he or she hasput in the ramen by voice, by saying “I have put in the ramen.”Accordingly, the user terminal 5000 can send a timer start signal to theelectric range 1000 (S170).

As another example, even if the user puts in the ramen without anysignal, the weight sensor of the electric range 1000 can automaticallysense that the ramen has been put in (S160). In response to thissensing, the electric range 1000 can ask the user for confirmation, orstart the timer without asking for confirmation.

Since the electric range 1000 has already received information about thecooking of the ramen from the user, the electric range 1000 can set thetimer to operate for 4 minutes from the time point when the ramen wasput in. After boiling the water for a further 4 minutes, the electricrange 1000 can stop the heating operation (S170). When a signalindicating that the heating operation has been stopped is transferred tothe user terminal 5000, the user terminal 5000 can inform the user thatthe cooking has been completed by saying “The ramen is ready.”

Next, FIG. 7 is a flowchart illustrating an operation of a heatingapparatus according to still another embodiment of the presentdisclosure. In particular, FIG. 7 illustrates when the user desires toboil a specific food by using the electric range 1000, and then maintainthe temperature for 5 minutes while preventing boiling over. As shown,the user can first make a voice command such as “Connect the electricrange” to the user terminal 5000, so that the electric range 1000 isconnected to the user terminal 5000. According to this voice command,the user terminal 5000 sends a connection command signal to the electricrange 1000 (S210).

The electric range 1000, having received the connection command signal,performs communication initialization and connection with the userterminal 5000 (S220). The user terminal 5000, having received theconnection confirmation signal, can perform a voice report of“Connected” to the user. The user who has confirmed that the userterminal 5000 and the electric range 1000 have been connected can say“Prevent boiling over for 5 minutes” regarding the cooking that he orshe is planning, thereby expressing the intention to boil the water andhave the water boil for a further 5 minutes after the water initiallystarts to boil while preventing boiling over.

Accordingly, the user terminal 5000 can send, to the electric range1000, a signal that sets power for boiling the object to be heated andsets a 5-minute timer (a timer set to boil the object to be heated for afurther 5 minutes after initially starting to boil) (S230). Whilesending the signal, the user terminal 5000 can notify the user of thedetails of the timer to be set by outputting, by voice, “When the waterboils, automatic power control will automatically operate for 5minutes.”

When receiving the signal, the electric range 1000 can confirm, throughthe weight sensor, whether the container containing the object to beheated has been placed on the top plate 120, and then start an operationat the maximum power level (S240). While heating the object to be heatedby supplying power to the heating member 130, the electric range 1000can sense that the object to be heated is boiling by receiving the soundsignal in the above-described manner, and start the 5-minute timer(S250).

The electric range 1000 can maintain the temperature of the object to beheated for 5 minutes while the object to be heated is prevented fromboiling over by the automatic power level control (S260). The electricrange 1000 can perform the automatic power level control for 5 minutesfrom the time point at which water initially started to boil, and thenstop the heating (S270).

Further, the user terminal 5000 can receive notification of the factthat the heating has been stopped, and inform the user that the cookinghas been completed by voice, such as “The cooking has been completed.”

Next, FIG. 8 is a diagram illustrating an automatic power level control,which is an operation for automatically controlling power after sensinga boiling state of the object to be heated in the heating apparatusaccording to an embodiment of the present disclosure. The controller 200of the electric range 1000 can start the automatic power control fromthe time point when it is sensed that the object to be heated isboiling, and can control the power level supplied to the heating member130 in the manner shown in FIG. 8 .

The time point when the power level is changed corresponds to a timepoint when there is a change in the sound signal, and the controller 200can increase or reduce the power level such that the magnitude of thesound signal received from the sound sensor is maintained within acertain range during a target time set by the user. After the targettime set by the user has elapsed, the controller 200 can reduce thepower level supplied to the heating member 130, or bring the power levelto zero.

FIG. 9 is a diagram illustrating a deep neural network model forpredicting a state of the object to be heated used in the heatingapparatus according to an embodiment of the present disclosure. Theelectric range 1000 can also use a deep neural network model that hasbeen trained in advance using machine learning, which is an area ofartificial intelligence, to estimate the state of the object to beheated through the sound signal.

Here, artificial intelligence (AI) is an area of computer engineeringscience and information technology that studies methods to makecomputers mimic intelligent human behaviors such as reasoning, learning,self-improving, and the like. Further, artificial intelligence does notexist on its own, but is rather directly or indirectly related to anumber of other fields in computer science. Particularly, in recentyears, there have been numerous attempts to introduce an element of AIinto various fields of information technology to solve problems in therespective fields.

Machine learning is an area of artificial intelligence that includes thefield of study that gives computers the capability to learn withoutbeing explicitly programmed. More specifically, machine learning is atechnology that investigates and builds systems, and algorithms for suchsystems, which are capable of learning, making predictions, andenhancing their own performance based on experiential data. Machinelearning algorithms, rather than only executing rigidly set staticprogram commands, may be used to take an approach that builds models forderiving predictions and decisions from input data.

Numerous machine learning algorithms have been developed for dataclassification in machine learning. Representative examples of suchmachine learning algorithms for data classification include a decisiontree, a Bayesian network, a support vector machine (SVM), an artificialneural network (ANN), and so forth.

Decision tree refers to an analysis method that uses a tree-like graphor model of decision rules to perform classification and prediction.Bayesian network may include a model that represents the probabilisticrelationship (conditional independence) among a set of variables.Bayesian network may be appropriate for data mining through unsupervisedlearning.

SVM may include a supervised learning model for pattern detection anddata analysis, heavily used in classification and regression analysis.ANN is a data processing system modeled after the mechanism ofbiological neurons and interneuron connections, in which a number ofneurons, referred to as nodes or processing elements, are interconnectedin layers.

ANNs are models used in machine learning and may include statisticallearning algorithms conceived from biological neural networks(particularly of the brain in the central nervous system of an animal)in machine learning and cognitive science. ANNs may refer generally tomodels that have artificial neurons (nodes) forming a network throughsynaptic interconnections, and acquires problem-solving capability asthe strengths of synaptic interconnections are adjusted throughouttraining. The terms ‘artificial neural network’ and ‘neural network’ maybe used interchangeably herein.

An ANN may include a number of layers, each including a number ofneurons. Furthermore, the ANN may include synapses that connect theneurons to one another. An ANN may be defined by the following threefactors: (1) a connection pattern between neurons on different layers;(2) a learning process that updates synaptic weights; and (3) anactivation function generating an output value from a weighted sum ofinputs received from a previous layer.

ANNs include, but are not limited to, network models such as a deepneural network (DNN), a recurrent neural network (RNN), a bidirectionalrecurrent deep neural network (BRDNN), a multilayer perception (MLP),and a convolutional neural network (CNN). An ANN may be classified as asingle-layer neural network or a multi-layer neural network, based onthe number of layers therein.

An ANN may be classified as a single-layer neural network or amulti-layer neural network, based on the number of layers therein. Ingeneral, a single-layer neural network may include an input layer and anoutput layer. In general, a multi-layer neural network may include aninput layer, one or more hidden layers, and an output layer.

The input layer receives data from an external source, and the number ofneurons in the input layer is identical to the number of inputvariables. The hidden layer is located between the input layer and theoutput layer, and receives signals from the input layer, extractsfeatures, and feeds the extracted features to the output layer. Theoutput layer receives a signal from the hidden layer and outputs anoutput value based on the received signal. Input signals between theneurons are summed together after being multiplied by correspondingconnection strengths (synaptic weights), and if this sum exceeds athreshold value of a corresponding neuron, the neuron may be activatedand output an output value obtained through an activation function.

A deep neural network with a plurality of hidden layers between theinput layer and the output layer may be the most representative type ofartificial neural network which enables deep learning, which is onemachine learning technique. An ANN may be trained using training data.Here, the training may refer to the process of determining parameters ofthe artificial neural network by using the training data, to performtasks such as classification, regression analysis, and clustering ofinput data. Such parameters of the artificial neural network may includesynaptic weights and biases applied to neurons. An artificial neuralnetwork trained using training data may classify or cluster input dataaccording to a pattern within the input data.

Throughout the present specification, an artificial neural networktrained using training data may be referred to as a trained model.Hereinbelow, learning paradigms of an artificial neural network will bedescribed in detail.

Learning paradigms, in which an artificial neural network operates, maybe classified into supervised learning, unsupervised learning,semi-supervised learning, and reinforcement learning. Supervisedlearning is a machine learning method that derives a single functionfrom the training data.

Among the functions that may be thus derived, a function that outputs acontinuous range of values may be referred to as a regressor, and afunction that predicts and outputs the class of an input vector may bereferred to as a classifier. In supervised learning, an artificialneural network may be trained with training data that has been given alabel. Here, the label may refer to a target answer (or a result value)to be guessed by the artificial neural network when the training data isinput to the artificial neural network.

Throughout the present specification, the target answer (or a resultvalue) to be guessed by the artificial neural network when the trainingdata is input may be referred to as a label or labeling data. Throughoutthe present specification, assigning one or more labels to training datain order to train an artificial neural network may be referred to aslabeling the training data with labeling data.

Training data and labels corresponding to the training data together mayform a single training set, and as such, they may be input to anartificial neural network as a training set. The training data mayexhibit a number of features, and the training data being labeled withthe labels may be interpreted as the features exhibited by the trainingdata being labeled with the labels. In this instance, the training datamay represent a feature of an input object as a vector.

Using training data and labeling data together, the artificial neuralnetwork may derive a correlation function between the training data andthe labeling data. Then, through evaluation of the function derived fromthe artificial neural network, a parameter of the artificial neuralnetwork may be determined (optimized).

Unsupervised learning is a machine learning method that learns fromtraining data that has not been given a label. More specifically,unsupervised learning may be a training scheme that trains an artificialneural network to discover a pattern within given training data andperform classification by using the discovered pattern, rather than byusing a correlation between given training data and labels correspondingto the given training data.

Examples of unsupervised learning include, but are not limited to,clustering and independent component analysis. Examples of artificialneural networks using unsupervised learning include, but are not limitedto, a generative adversarial network (GAN) and an autoencoder (AE).

GAN is a machine learning method in which two different artificialintelligences, a generator and a discriminator, improve performancethrough competing with each other. The generator may be a modelgenerating new data that generates new data based on true data.

The discriminator may be a model recognizing patterns in data thatdetermines whether input data is from the true data or from the new datagenerated by the generator. Furthermore, the generator may receive andlearn from data that has failed to fool the discriminator, while thediscriminator may receive and learn from data that has succeeded infooling the discriminator. Accordingly, the generator may evolve so asto fool the discriminator as effectively as possible, while thediscriminator evolves so as to distinguish, as effectively as possible,between the true data and the data generated by the generator.

An auto-encoder (AE) is a neural network which aims to reconstruct itsinput as output. More specifically, AE may include an input layer, atleast one hidden layer, and an output layer. Since the number of nodesin the hidden layer is smaller than the number of nodes in the inputlayer, the dimensionality of data is reduced, thus leading to datacompression or encoding.

Furthermore, the data output from the hidden layer may be input to theoutput layer. Given that the number of nodes in the output layer isgreater than the number of nodes in the hidden layer, the dimensionalityof the data increases, thus leading to data decompression or decoding.

Furthermore, in the AE, the input data is represented as hidden layerdata as interneuron connection strengths are adjusted through training.The fact that when representing information, the hidden layer canreconstruct the input data as output by using fewer neurons than theinput layer may indicate that the hidden layer has discovered a hiddenpattern in the input data and is using the discovered hidden pattern torepresent the information.

Semi-supervised learning is machine learning method that makes use ofboth labeled training data and unlabeled training data. This techniquemay be used advantageously when the cost associated with the labelingprocess is high.

Reinforcement learning may be based on a theory that given the conditionunder which a reinforcement learning agent may determine what action tochoose at each time instance, the agent may find an optimal path to asolution solely based on experience without reference to data.Reinforcement learning may be performed mainly through a Markov decisionprocess.

Markov decision process consists of four stages: first, an agent isgiven a condition containing information required for performing a nextaction; second, how the agent behaves in the condition is defined;third, which actions the agent should choose to get rewards and whichactions to choose to get penalties are defined; and fourth, the agentiterates until future reward is maximized, thereby deriving an optimalpolicy.

Also, the hyperparameters are set before learning, and model parametersmay be set through learning to specify the architecture of theartificial neural network. For instance, the structure of an artificialneural network may be determined by a number of factors, including thenumber of hidden layers, the number of hidden nodes included in eachhidden layer, input feature vectors, target feature vectors, and soforth.

Hyperparameters may include various parameters which need to beinitially set for learning, much like the initial values of modelparameters. Also, the model parameters may include various parameterssought to be determined through learning. For instance, thehyperparameters may include initial values of weights and biases betweennodes, mini-batch size, iteration number, learning rate, and so forth.Furthermore, the model parameters may include a weight between nodes, abias between nodes, and so forth.

Loss function may be used as an index (reference) in determining anoptimal model parameter during the learning process of an artificialneural network. Learning in the artificial neural network involves aprocess of adjusting model parameters so as to reduce the loss function,and the purpose of learning may be to determine the model parametersthat minimize the loss function. Loss functions typically use meanssquared error (MSE) or cross entropy error (CEE), but the presentdisclosure is not limited thereto.

Cross-entropy error may be used when a true label is one-hot encoded.One-hot encoding may include an encoding method in which among givenneurons, only those corresponding to a target answer are given 1 as atrue label value, while those neurons that do not correspond to thetarget answer are given 0 as a true label value. In machine learning ordeep learning, learning optimization algorithms may be deployed tominimize a cost function, and examples of such learning optimizationalgorithms include gradient descent (GD), stochastic gradient descent(SGD), momentum, Nesterov accelerate gradient (NAG), Adagrad, AdaDelta,RMSProp, Adam, and Nadam.

GD includes a method that adjusts model parameters in a direction thatdecreases the output of a cost function by using a current slope of thecost function. The direction in which the model parameters are to beadjusted may be referred to as a step direction, and a size by which themodel parameters are to be adjusted may be referred to as a step size.Here, the step size may mean a learning rate.

GD obtains a slope of the cost function through use of partialdifferential equations, using each of model parameters, and updates themodel parameters by adjusting the model parameters by a learning rate inthe direction of the slope. SGD may include a method that separates thetraining dataset into mini batches, and by performing gradient descentfor each of these mini batches, increases the frequency of gradientdescent.

Adagrad, AdaDelta and RMSProp may include methods that increaseoptimization accuracy in SGD by adjusting the step size, and may alsoinclude methods that increase optimization accuracy in SGD by adjustingthe momentum and step direction. Adam may include a method that combinesmomentum and RMSProp and increases optimization accuracy in SGD byadjusting the step size and step direction. Nadam may include a methodthat combines NAG and RMSProp and increases optimization accuracy byadjusting the step size and step direction.

Learning rate and accuracy of an artificial neural network rely not onlyon the structure and learning optimization algorithms of the artificialneural network but also on the hyperparameters thereof. Accordingly, inorder to obtain a good learning model, it is important to choose aproper structure and learning algorithms for the artificial neuralnetwork, but also to choose proper hyperparameters.

In general, the artificial neural network is first trained byexperimentally setting hyperparameters to various values, and based onthe results of training, the hyperparameters may be set to optimalvalues that provide a stable learning rate and accuracy. It is possibleto further refine the estimation of the state of the object to be heatedby using the above-described methods.

Although there may be various methods for generating the deep neuralnetwork model to be used in an embodiment of the present disclosure, inthe case of supervised learning, the following training process may beperformed as preliminary work. After installing an apparatus capable ofdirectly measuring the temperature of the contents of a pot and amicrophone capable of collecting the sound generated from the pot on thetop plate of the electric range, the temperature of the contents and thesound generated at the corresponding temperature may be recorded whilechanging, for example, the type, weight, and size of the pot, and thetype, weight, and size of contents contained in the pot.

The recorded data is sound data for which the corresponding temperatureis labeled, and the deep neural network model may learn using thelabeled data. Specifically, a deep neural network model capable ofestimating the temperature of the contents according to the soundgenerated from the pot on the top plate of the electric range may betrained with the labeled data.

The deep neural network model generated through this preliminary workmay be embedded in the memory of the electric range 1000, or stored inthe external server 7000 with which the electric range 1000communicates, and may be used to estimate the state of the object to beheated according to the sound signal received from the sound sensor 170of the electric range 1000 during actual use.

Information such as sound information collected by the electric range1000 from the object to be heated which is being heated, information ofthe type of cooking input by the user, and weight information sensed bythe weight sensor 160 may be input to the deep neural network model thatis trained as described above, and accordingly, the current state of theobject to be heated or an estimation result relating to the degree ofboiling of the object to be heated may be output.

Meanwhile, the input information may include various information such asthe material of the container, the shape of the container, and the typeof contents, in addition to the information described in FIG. 9 . Inthis instance, it is natural that a deep neural network model suitablefor such input information may be trained and used.

The example embodiments described above may be implemented throughcomputer programs executable through various components on a computer,and such computer programs may be recorded in computer-readable media.Examples of the computer-readable media include, but are not limited to:magnetic media such as hard disks, floppy disks, and magnetic tape;optical media such as CD-ROM disks and DVD-ROM disks; magneto-opticalmedia such as floptical disks; and hardware devices that are speciallyconfigured to store and execute program codes, such as ROM, RAM, andflash memory devices.

The computer programs may be those specially designed and constructedfor the purposes of the present disclosure or they may be of the kindwell known and available to those skilled in the computer software arts.Examples of program code include both machine code, such as produced bya compiler, and higher level code that may be executed by the computerusing an interpreter.

As used in the present application (especially in the appended claims),the terms ‘a/an’ and ‘the’ include both singular and plural references,unless the context clearly states otherwise. Also, it should beunderstood that any numerical range recited herein is intended toinclude all sub-ranges subsumed therein (unless expressly indicatedotherwise) and accordingly, the disclosed numeral ranges include everyindividual value between the minimum and maximum values of the numeralranges.

Also, the order of individual steps in process claims of the presentdisclosure does not imply that the steps must be performed in thisorder; rather, the steps may be performed in any suitable order, unlessexpressly indicated otherwise. In other words, the present disclosure isnot necessarily limited to the order in which the individual steps arerecited. All examples described herein or the terms indicative thereof(“for example”, etc.) used herein are merely to describe the presentdisclosure in greater detail. Accordingly, it should be understood thatthe scope of the present disclosure is not limited to the exampleembodiments described above or by the use of such terms unless limitedby the appended claims. Also, it should be apparent to those skilled inthe art that various alterations, substitutions, and modifications maybe made within the scope of the appended claims or equivalents thereof.

The present disclosure is thus not limited to the example embodimentsdescribed above, and rather intended to include the following appendedclaims, and all modifications, equivalents, and alternatives fallingwithin the spirit and scope of the following claims.

What is claimed is:
 1. A method of controlling a heating apparatus, themethod comprising: supplying power to a heating member of the heatingapparatus to heat an object; detecting, via a vibration sensor disposedunder a top plate of the heating apparatus, a vibration signal generatedwhen the object is heated by the heating member; determining, via anartificial intelligence model having learned properties of the vibrationsignal, whether the object to be heated is boiling; and adjusting thepower supplied to the heating member based on the determination ofwhether the object to be heated is boiling.
 2. The method of claim 1,wherein: at least a portion of a bottom surface of the top plate has anembossed shape, the vibration sensor is disposed to contact the bottomsurface of the top plate, and at least a portion of the bottom surfaceof the top plate on which the vibration sensor is disposed has a flatshape so that the vibration sensor closely contacts the bottom surfaceof the top plate.
 3. The method of claim 2, further comprising:determining if the object to be heated is boiling by using a deep neuralnetwork model that has been trained in advance to determine if theobject to be heated is boiling based on the vibration signal detected bythe vibration sensor.
 4. The method of claim 1, further comprising:sensing, via a weight sensor disposed to contact a surface of the topplate, a weight of the object to be heated that is supported by the topplate; and determining whether the object to be heated is boiling basedon the weight sensed by the weight sensor and the vibration detected bythe vibration sensor.
 5. The method of claim 4, further comprising:determining an additional cooking ingredient has been added or removedto the object based on the weight sensed by the weight sensor.
 6. Themethod of claim 1, further comprising: sensing, via a weight sensordisposed to contact a bottom surface of the top plate, a weight of theobject to be heated that is supported by the top plate; if no additionalweight change is sensed after a predetermined time period, reducingpower supplied to the heating member; if an increased weight change issensed during the predetermined time period, maintaining the powersupplied to the heating member or increasing the power supplied to theheating member; if a reduced weight change is sensed after thepredetermined time period, reducing the power supplied to the heatermember including turning off the power and stop detecting the vibrationsignal; if a reduced weight between a first weight and a second weightis sensed, reducing the power supplied to the heating member by a firstamount, and if a reduced weight between the second weight and a thirdweight is then sensed, reducing the power supplied to the heating memberby a second amount; and if an increased weight is sensed between thefirst weight and the second weight, increasing the power to the heatingmember to maintain a boiling state of the object to be heated.
 7. Themethod of claim 1, wherein the heating member is disposed to contact abottom surface of the top plate, wherein the heating member includes acoil having a hollow formed in the center thereof, and wherein thevibration sensor is disposed to contact the bottom surface of the topplate at a position where the hollow is formed.
 8. The method of claim1, further comprising: communicating, via a transceiver included in theheating apparatus, with a terminal; and transmitting an alarm signal tothe terminal through the transceiver when the object to be heated isdetermined to be boiling.
 9. The method of claim 8, further comprising:receiving, via the transceiver, from the terminal, information on atarget time during which boiling is to be maintained; controlling thepower supplied to the heating member such that a magnitude of thevibration signal of the vibration detected by the vibration sensor ismaintained within a certain range during the target time from a timepoint at which the object is determined to be boiling; and reducing thepower supplied to the heating member after the target time has elapsed.10. A non-transitory computer readable medium embodied on a computerreadable medium and storing instructions that when executed by aprocessor, perform the following: controlling a power supply to supplypower to a heating member of a heating apparatus to heat an object;receiving, from a vibration sensor disposed under a top plate of theheating apparatus, a vibration signal generated when the object isheated by the heating member; using an artificial intelligence modelhaving learned properties of the vibration signal to determine whetherthe object to be heated is boiling; and controlling the power supply toadjust the power supplied to the heating member based on thedetermination of whether the object to be heated is boiling.